Interpreting Large Language Model Behavior Using Shapley Value Analysis: Uncovering the Impact of Token Noise on Decision-Making
Large language models (LLMs) exhibit decision-making patterns that can diverge significantly from human cognition, raising concerns about their validity as proxies for human subjects in research. This paper presents a novel Shapley value-based approach to quantify the relative contribution of each prompt component in shaping LLM outputs, revealing the outsized impact of "token noise" - tokens with minimal informative content - on LLM decisions. The findings underscore the need for a more nuanced understanding of LLM behavior and caution against over-relying on these models as substitutes for human subjects.
Comprehensive Review of Multi-Modal Large Language Models: Advancements, Challenges, and Ethical Considerations
This review provides an in-depth analysis of the current state of multi-modal large language models (MM-LLMs), covering their historical development, technical advancements, applications, and ethical considerations. It examines the role of attention mechanisms, the benefits and drawbacks of proprietary versus open-source models, and the latest innovations in MM-LLMs such as BLIP-2, LLaVA, Kosmos-1, MiniGPT4, and mPLUG-OWL.
Optimizing Energy Efficiency in Large Language Model Inference Serving
Achieving energy efficiency in large language model (LLM) inference serving without compromising performance is crucial for sustainable and cost-effective deployment of these models in data centers.
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